自适应引导上采样，实时提升暗光图像质量
Adaptive Guided Upsampling for Low-light Image Enhancement
- 基于多参数优化学习暗光与明亮图像特征关联
- 仅需少量样本对即可实现噪声抑制与锐化同步优化
- 适用于实时暗光图像增强，性能优于现有方法
我们提出自适应引导上采样（AGU），一种高效低光图像上采样方法，可同时优化多种图像质量特性，如降噪和锐化。传统引导图像方法依赖引导图像传递特征，但当前低光图像因噪声高、亮度低，难以有效传递特征，导致增强效果不佳。为此，我们采用多参数优化策略，学习低光与明亮图像间的多重特征关联，并通过少量样本图像对训练机器学习模型。AGU 能在低质量、低分辨率输入下实时生成高质量图像，实验表明其在低光场景下优于现有最先进方法。
原文摘要 · Abstract (English)
We introduce Adaptive Guided Upsampling (AGU), an efficient method for upscaling low-light images capable of optimizing multiple image quality characteristics at the same time, such as reducing noise and increasing sharpness. It is based on a guided image method, which transfers image characteristics from a guidance image to the target image. Using state-of-the-art guided methods, low-light images lack sufficient characteristics for this purpose due to their high noise level and low brightness, rendering suboptimal/not significantly improved images in the process. We solve this problem with multi-parameter optimization, learning the association between multiple low-light and bright image characteristics. Our proposed machine learning method learns these characteristics from a few sample images-pairs. AGU can render high-quality images in real time using low-quality, low-resolution input; our experiments demonstrate that it is superior to state-of-the-art methods in the addressed low-light use case.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。